Release Time:2019-03-11  Hits:

Indexed by: Conference Paper

Date of Publication: 2011-04-15

Included Journals: Scopus、EI

Page Number: 3410-3413

Abstract: Appropriate parameters are very crucial to the learning performance and generalization ability of least-squares support vector machines (LS-SVM). In this paper, a novel parameter selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. The selection problem of LS-SVM parameters is considered as a compound optimization problem. Then objective function of optimization problem is set and a CAS optimization algorithm is employed to search optimal objective function. CAS algorithm is global search method and it need not to consider LS-SVM dimensionality and complexity. The simulation results show that the proposed method is an effective approach for parameter optimization and the good performance for function approximation is obtained. ? 2011 IEEE.